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제주도에서의 위성기반 증발산량 및 토양수분 적용성 평가
전현호,조성근,정일문,최민하,Jeon, Hyunho,Cho, Sungkeun,Chung, Il-Moon,Choi, Minha 한국수자원학회 2021 한국수자원학회논문집 Vol.54 No.10
In Jeju Island which has peculiarity for its geological features and hydrology system, hydrological factor analysis for the effective water management is necessary. Because in-situ hydro-meteorological data is affected by surrounding environment, the in-situ dataset could not be the spatially representative for the study area. For this reason, remote sensing data may be used to overcome the limit of the in-situ data. In this study, applicability assessment of MOD16 evapotranspiration data, Globas Land Data Assimilation System (GLDAS) based evapotranspiration/soil moisture data, and Advanced SCATterometer (ASCAT) soil moisture product which were evaluated their applicability on other study areas was conducted. In the case of evapotranspiration, comparison with total precipitation and flux-tower based evapotranspiration were conducted. And for soil moisture, 6 in-situ data and ASCAT soil moisture product were compared on each site. As a result, 57% of annual precipitation was calculated as evapotranspiration, and the correlation coefficient between MOD16 evapotranspiration and GLDAS evapotranspiration was 0.759, which was a robust value. The correlation coefficient was 0.434, indicating a relatively low fit. In the case of soil moisture, in the case of the GLDAS data, the RMSE value was less than 0.05 at all sites compared to the in-situ data, and a statistically significant result was obtained as a result of the significance test of the correlation coefficient. However, for satellite data, RMSE over than 0.05 were found at Wolgak and there was no correlation at Sehwa and Handong points. It is judged that the above results are due to insufficient quality control and spatial representation of the evapotranspiration and soil moisture sensors installed in Jeju Island. It is estimated as the error that appears when adjacent to the coast. Through this study, the necessity of improving the existing ground observation data of hydrometeorological factors is emphasized.
위성 정보를 활용한 도심 지역 기온자료 지도화를 위한 인공신경망 적용 연구
전현호,정재환,조성근,최민하,Jeon, Hyunho,Jeong, Jaehwan,Cho, Seongkeun,Choi, Minha 한국수자원학회 2022 한국수자원학회논문집 Vol.55 No.11
In this study, the Artificial Neural Network (ANN) was used to mapping air temperature in Seoul. MODerate resolution Imaging Spectroradiomter (MODIS) data was used as auxiliary data for mapping. For the ANN network topology optimizing, scatterplots and statistical analysis were conducted, and input-data was classified and combined that highly correlated data which surface temperature, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), time (satellite observation time, Day of year), location (latitude, hardness), and data quality (cloudness). When machine learning was conducted only with data with a high correlation with air temperature, the average values of correlation coefficient (r) and Root Mean Squared Error (RMSE) were 0.967 and 2.708℃. In addition, the performance improved as other data were added, and when all data were utilized the average values of r and RMSE were 0.9840 and 1.883℃, which showed the best performance. In the Seoul air temperature map by the ANN model, the air temperature was appropriately calculated for each pixels topographic characteristics, and it will be possible to analyze the air temperature distribution in city-level and national-level by expanding research areas and diversifying satellite data.
자율주행 트랙터를 위한 조향 제어기 시뮬레이션 모델 개발에 관한 연구
전현호 ( Hyeon-ho Jeon ),김완수 ( Wan-soo Kim ),김용주 ( Yong-joo Kim ) 한국농업기계학회 2020 한국농업기계학회 학술발표논문집 Vol.25 No.1
자율주행 트랙터는 사람이 탑승하지 않고 작업을 하기 때문에 조향 시스템에 대한 성능평가가 필수적이다. 본 연구에서 사용 된 조향제모델은 횡방향 오차를 실시간으로 수신받아 횡방향 오차에 대한 PI 제어를 수행하게 설계되었다. 동역학 모델은 Recurdyn(V9R2, Functionbay, Korea)를 이용하여 구성하였다. 시뮬레이션 조건은 작업시와 비작업시의 제어모델의 성능평가를 수행하기 위해 동일단수에서의 주행조건 및 쟁기작업시로 선정하였다. 농작업 부하를 반영하기 위해 주행시 및 이랑쟁기 작업간 트랙터의 차축토크를 계측하고, 이를 시뮬레이션 입력값으로 설정하였다. 성능평가지표로는 횡방향 오차(Lateral error), 조향각(Steering angle) 및 방위각 오차)Heading angle error)로 선정하였다. 시뮬레이션 결과 주행조건에서는 횡방향 오차는 평균 0.49 mm, 방위각 오차는 0.06° 및 조향각은 평균 5.22°로 나타났다. 이랑쟁기작업조건에서는 횡방향 오차는 평균 0.59 mm, 방위각 오차는 0.13° 및 조향각은 평균 2.15°로 나타났다. 이랑쟁기 작업시 횡방향 오차는 0.1 mm, 방위각 오차는 0.07° 및 조향각은 3.07° 차이를 보였다. 본 연구에서 개발된 제어모델은 횡방향오차만 고려하기 때문에 방위각오차 및 조향각을 고려한 모델에 비하여 정상상태로 진입하는 시간이 더 오래걸린다. 따라서, 차후연구에서는 방위각 오차 및 조향각을 고려한 개선된 제어기 개발이 필요할 것으로 판단되며, 실차모델 구성을 통한 실차평가가 수행되어야 한다고 판단된다.
전현호 ( Hyeon-ho Jeon ),김용주 ( Young-joo Kim ) 한국농업기계학회 2022 한국농업기계학회 학술발표논문집 Vol.27 No.1
Agricultural operation using tractors is mainly conducted in paddy fields or unpaved roads. Operations in paddy fields or unpaved roads creates constant vibrations. The vibrations on a tractor cabin is important regarding the comfort and helath of the operator. Therfore, in order to increase the comfort and health of the operator, research on reducing the vibration on tractor cabin is required. In the case of small and medium-sized tractors used in Korea, there is no vibration control technology such as cabin or axle suspension. Therefore, the role of reducing the vibration of tractor is conducted only by the vehicle's tires. In order to increase comfort of the operator, it is necessary to select an appropriate tire. For this purpose, a lot of tests using real vehicles are generally performed, but these methods are high time and cost consuming. Studies using dynamic models are needed to reduce the cost and time spent. This research is a basic study for developing a dynamic model of an agricultural tractor. A measurement system for measuring the stiffness coefficient of tires was developed. A 38 kW class tractor (M520, Tongyangmoolsan., Co., Ltd., Korea) was selected for the stiffness coefficient measurement. The frame for fixing the tire for measurement was constructed as shown in Fig 1. The tire was fixed on the frame, and a load cell and displacement sensor were attached to measure the amount of tire deformation according to the load. The data of the sensor was measured using DAQ (840B, HBM, Germany). The data measurement was repeated three times. It shows that the stiffness coefficient of the front tire was about 449 and the rear tire was about 643. In the future study, other coefficients such as dampring ratio will be measured to develop the dynamic model of an agricultural tractor.z